Building Privacy-Focused Local RAG Systems: Self-Hosted AI Solutions for Data-Sensitive Organizations
By
pedriquepacheco
Master baker tier. Every paragraph earns its place on the tray.
Summary
The article discusses Skald's approach to building a local RAG (Retrieval-Augmented Generation) system that prioritizes data privacy and self-hosting capabilities. It explains how organizations with privacy concerns can use open-source alternatives to proprietary AI APIs without compromising on data security. The content covers RAG components, compares performance between proprietary and self-hosted solutions, and provides benchmarks to demonstrate the viability of privacy-focused AI implementations.
Key quotes
· 4 pulledWhen we launched Skald, we wanted it to not only be self-hostable, but also for one to be able to run it without sending any data to third-parties.
With LLMs getting better and better, privacy-sensitive organizations shouldn't have to choose between being left behind by not accessing frontier models and doing away with their commitment to (or legal requirement for) data privacy.
So here's what we did to support this use case and also some benchmarks comparing performance when using proprietary APIs vs self-hosted open-source tech.
A basic RAG usually...
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